Credit Ss & Agric Output

EMPIRICAL ANALYSIS OF CREDIT SUPPLY AND AGRICULTURAL OUTPUT IN NIGERIA BERNARD, OJONUGWA ANTHONY

Department Of Economics Kogi State University, Anyigba. Email: [email protected] 08065499711, 08070539895

Abstract A strong agricultural sector would enable a country like Nigeria to meet the challenges of the recent economic crises ravaging the whole world by providing food for the teeming population, generate employment, foreign exchange earnings and raw materials for industries. The paper therefore, empirically analyse the effects of Credit Supply on Agricultural Output in Nigeria. This study uses the time series data that span a period of 23years (1986-2008). The study specifies a Multiple regression Loglinear Model (base on the theoretical framework of Cobb-Douglas production function) with four explanatory variables. That is bank loans and advances, government capital expenditure on agriculture, agricultural credit guarantee scheme and foreign investment on agriculture. The study makes use of the OLS method to test the significance of the explanatory variables on output of agricultural sector in Nigeria. The result revealed that except the foreign direct investment on agriculture, other variables expressed significant influence on agricultural output in Nigeria. The researcher concludes that, there is need to enhance and monitor credit supplied for agricultural purpose to effectively attain the expected growth in the sector.

Introduction Agricultural sector in Nigeria was the most dominant sector before the early 1970s. Until the early 1970s, it was the major development drive of the economy employing over 80% of the active population. (Anyanwu et’al, 1997). It also contributed to over 60% of the nation’s Gross Domestic Product (GDP) and provided nearly 100% of the economy’s food requirement; raw materials to industries and the country’s export earnings among others. Prior to early 1970s there were significant growth in this sector, but during the oil boom era when crude oil became a major export earner, agriculture began to falter as its contribution to GDP began to decline from over 60% in early 1970 to 30% and 40% (Aigbokhan, 2001) and less than 1

26% between 2000 and 2007(CBN, 2007). These indicate that the discovery of oil as the fastest means of revenue brought about devastating neglect of the agricultural sector. Due to the aforementioned problems, over a decade, most government policies have been directed towards accelerating economic development with the ultimate aim of transforming the economy into an industrialized one, as well as raising the welfare of the people. One of the sectors expected to act as a catalyst towards the realization of this goal is the agricultural sector. This is measured by increasing the output of agricultural sector to meet the demand of the people and the industries. In order to increase the output of agricultural sector, government over the years has been given priority to agriculture in its budget, directing financial institution to make credit available to farmers. Agricultural credit is expected to play a vital role in agricultural development (Duong and Izumida, 2002). Agricultural Credit has over the years been identified as a major input in the development of the agricultural sector in Nigeria (CBN, 2005). The decline in the contribution of the sector to the Nigerian economy has been attributed to the lack of a formal national credit policy and paucity of credit institution, which can assist farmers in the purchase of farm inputs (Rahji and Fakayode, 2009). The provision of these input by the sector is important because credit or loan-able fund helps in determining access to all the needed inputs to facilitate farming. Access to agricultural credit has been severely constrained in developing countries. This is because of the imperfection and costly information problems encountered in the financial markets (Swinnen and Gow, 1999). Such problems are common and particularly important in agriculture (Stiglitz, 1993). To ameliorate the prevailing problem of credit supply to agricultural sector, government of Nigeria came up with the Agricultural Credit Guarantee Scheme (ACGS) in 1978, with the objective of providing guarantees in 2

respect of loans granted for agricultural purposes by any bank in accordance with the provisions of the Act and with the aim of increasing the level of bank credit to the agricultural sector (Anyanwu et’al 1997). In addition, were the gentle appeals to make loans and advances available to agricultural sector by commercial banks. As the major objective of this research, we shall empirically estimate how the various credit supplied by the government and other financial institutions have contributed to the output growth of agricultural sector in Nigeria. While this section introduces the subject, section 2 and 3 are for literature review and methodology of research respectively. Section 4 estimates the model for this study, hypotheses are tested and regression results are analyzed. Section 5 concludes the work and provides policy recommendation that will boast agricultural development in Nigeria. Literature review Theoretical Literature Ekpebu (2006), reviews that the performance of the agricultural sector has been unsatisfying over the years due to insufficient funding or credit facilities, inadequate infrastructural facilities, low technology base, high cost of farm input and inadequate extension services. Trzeciak-Daveal (2003), in his own view opined that agriculture like all other sectors of the economy needs credit for its development. Experience was drawn from Organization for Economic

Cooperation

and

Development

(OECD)

countries.

He

demonstrated that in a competitive financial environment, profitable agriculture can obtain the credit it needs, also suggested that government have a vital role in channeling fund to agricultural sector through its policy making. Radolphe (2005), bringing together loan commitment theories and credit rationing theories, within a framework of asymmetric information 3

between lenders and borrowers and under costly termination of lending arrangements, commitment may explain the accumulation of non performing loans by banks. In this theory, two additional results follow: That banks favour borrowers with well known production functions and long-term credit history and that interest rate may be large if significant market imperfection prevail. In agricultural household models, farm credit is not only necessitated by the limitation of self-finance and government expenditure, but also by uncertainty pertaining to the level of farm inputs and outputs and the time lag between inputs and outputs (Sighh et’al 1980). CBN (2003) identified access to agricultural credit as factor responsible for the sustainable growth in the agricultural sector. Also, government has a vital role in the growth of agricultural sector in Nigeria (Obiechina, 2007). Ekechi (1977) supported the view that raising the volume of financial savings will increase the volume of total deposit of the banking sector which will further lead to increase in the supply of credit to other sectors of the economy (agricultural sector inclusive). The theoretical basis of this study is anchored on the 2-Gap models or the Harrod-Domar model and the Cobb-Douglas production function. According to Harrod-Domar model, there exists a domestic saving gap and foreign exchange gap in developing countries. The domestic savings gap exist when the domestic savings capacity falls bellow that necessary

to

permit the level of investment required to achieve a particular rate of growth in the economy. While available inputs are adequate. In this situation foreign financial resources cover this gap or make up the deficit and permit achievement of the expected growth rate. By implication, the foreign exchange gap exists if with adequate domestic savings, the flow of import is not sufficient because there is inadequate foreign exchange to finance it. Again, foreign capital breaks the import bottleneck and permits the target growth rate to be realized. 4

The Cobb-Douglas (CD) production function is also a substantial guidance for specifying supply–side agricultural potential output which is primarily determined by measurable input factor (Q=AkαLß). This theory is to a large extent consistent with the theory of supply of production function that underlies specification of the supply-side of agricultural output. The CobbDouglas (CD) production function was derived from the observation by Cobb (1928) and Douglas (1948) that over the long-run, the relative share of National Output earned by Labour (L) and Capital (K) tends to be constant. The CD function further assumes constant returns to scale and unitary elasticity of substitution. The CD production is generally given by the equation: Q = AKβLα Where: Q = Total Output K = Capital L = Labour A Efficiency Factor β and α = Substitution Parameter β = (1- α) and β + α = 1

1

Linear homogeneity of CD Production Function If we increase each factor in equation (1) by a constant λ, we have Q = A (λK)β (λL)α

2

Q = Aλβ + α KβLα Q = λAKβLα ( since β + α =1)

3

Therefore, λ = 1 From equation (3), we observed that the CD production is linearly homogeneous in Labour and Capital. This implies that, if we increase all inputs by a constant multiple (λ), output will increase by that same constant. Thus the Cobb-Douglas function is to be characterised by constant return to scale.

5

Slope and Convexity of Isoquant dK = ‫־‬β/L = - β . K dL α/K α L Strict Convexity is established by the expression dK2 = ‫־‬β . 1 LdK – K > 0 dL2 α L2 dL

4

5

Therefore, Isoquant is Strictly Convex Average and Marginal Physical Product From equation (1) APPL = Q = AKβLα = AKβLα – 1 L L APPk = Q = AKβLα = AKβ -1Lα K K

6 7

MPPL = ∂Q = α AKβLα – 1 ∂L MPPK = ∂Q = βAKβ - 1Lα ∂K Output Elasticity of Inputs

8 9

Output Elasticity of Capital MPPK = βAKβ - 1Lα = β APPk AKβ -1Lα

10

MPPL = α AKβLα – 1 = α APPL AKβLα – 1

11

Elasticity of Substitution Assuming that firms behave rationally (minimizing cost) then, ∂Q ∂L w

∂Q ∂K

r

12

Where w = wage rate and r = unit cost of Capital ∂K = w ∂L r

13

From equation (4) above, α = ∂K . L

14 6

β

∂L K

Consequently, K = β . ∂K 15 L α ∂L Combining equation (13) and (14), we have K = β .w L α r This implies that a given percentage change in ∂K will lead to an equal percentage in ∂L Input ratios. However, due to the peculiarity of the objectives of the study, the specification of the production function shall incorporate variables such as; commercial banks loans and advances to agricultural sector, government capital expenditure on agriculture, agricultural credit guarantee scheme and foreign direct investment on agriculture. Most of the variables are not as specified by Harrod-Domar and Cobb-Douglas but are regarded as capital and fund for investment that strongly influence the domestic output growth. Empirical Literature Otu and Balogun (1991) in their study of credit policies and agricultural development in Nigeria tested two hypotheses that credit policies influence to a large extent the behaviour of both constitutional lenders and borrowers. That is, credit policies can influence favourably the supply and demand for agricultural credit. Secondly, that a positive relationship exists between agricultural credit and a host of other variables such as output and use of modern inputs. Empirically they concluded that credit policies play very little role in influencing both lenders and borrowers behaviour. Credit subsidies are also major sources of production disincentive. They further contend that there is need to re-examine the overall objective of agricultural credit policies largely because it will be erroneous to infer that finance plays little role in agricultural development of the economy. Raji and Fakayode (2009) tried to identify the determinants influencing commercial banks decision to ration 7

credit in South-Western Nigeria. Data analyzed were from agricultural credit transaction of banks in Nigeria. Evidence from the multinomial model estimated shows that borrowers are heterogeneous. Akpan (1999) uses time series data of 33 years, and the OLS method of regression to analyze the contribution of government expenditures to the growth process in Nigeria. He concluded that capital expenditure on agriculture though not statistically significant but influence positively on investment. Oguamanam (1996) did an empirical work on commercial bank credit to agriculture sector in Nigeria. From the analysis, commercial bank loans and advances has positive relationship with the level of agricultural output, federal government capital expenditure contributed positively to the growth of agricultural output in Nigeria. Similar work was carried out by Nnanna (2001), on bank lending behaviour and output growth with implication on monetary policy in Nigeria. He revealed a significant relationship between banks lending behaviour and output growth. He further suggested that in the medium-term, the decline in output has negative influence on bank credit to private sector. Also Isijola (2000) revealed a significant relationship between credit supply and agricultural output in Nigeria. isijola also identified commercial banks’ loans and advances, Agricultural Credit Guaranteed Scheme as the determinant of agricultural credit supply in Nigeria. Shanggen et’al (1998) in their empirical analysis on government spending, growth and poverty, supported the view that government spending enhance the growth in agricultural productivity. His managerial analysis also shows that additional government expenditures on agricultural research and extension have the largest impact on agricultural productivity growth. Conclusively, this study deviates a little bit from the studies reviewed by segregating activities thereby looking at the effect of credit supply on agricultural output in Nigeria. 8

Methodology of Research Secondary data on credit supply and agricultural outputs are employed for this study. The data are obtained from Central Bank of Nigeria (CBN) publications. This study makes use of analytical tools which consist of the use of ordinary least square (OLS) regression. The research adopts the Harrod-Domar model but in a modified form based on the theoretical assertion for this study. Specification of Model and Definition of Variables Q = α BL 1GE 2AC 3FI 4℮µ…………………………….1 β

β

β

β

Applying the logarithm transformation: InQ = lnα +β 1lnBL+β 2lnGE+β 3lnAC+β 4lnFI+µ…………..2 Note lne =1, therefore, eµ = µ and ln =logarithmic Q = Output of major Agricultural Commodities (staples and other crops) BL = Bank’s loan and advances to Agricultural sector GE = Government Capital Expenditure on Agricultural Sector AC = Agricultural Credit Guarantee Scheme Fund FI = Foreign Direct Investment on Agriculture α = Intercept term. β

1,

β

2,

β

3

and β

4

= Elasticity of Output (Q) or the Coefficients of the

variables. µ = error term. The sum of the estimated coefficients (β 1+β 2+β 3+β 4) gives the homogeneity of the functions. If the sum is = 1, we have a constant return to scale in agricultural output, If >1 we have an increasing return to scale and

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<1, we have a decreasing return to scale in output. On the a priori, β 1>0, β 2>0, β 3>0 and β 4 >0. Q = lnQ α = lnα BL =lnBL GE =lnGE AC =lnAC FI = lnFI Plug these into equation 2. Q* = α *+β 1BL*+β 2GE*+β 3AC*+β 4FI*+µ ……………3 Therefore, the OLS can be applied to the linearised model (eqn 3) to obtain the estimate of the coefficients. The (*) indicates natural logarithms, Hypothesis to be tested are: β

1

=0, credit supply has significantly influenced the output of agriculture in Nigeria

β 1 ≠ 0, Credit supply has not significantly influenced the output of agricultural credit in Nigeria. Where: β i = 1-4.

Estimation of Model and Analysis of Results. Estimation of Model: Variables

Coefficient

β Constant BL GE AC FI

0.355 0.501 0.365 0.683 -0.357

Std. Error 2.981 0.262 0.230 0.289 0.360

t 0.119 1.910 1.584 2.363 -1.034

Change Statistics R Square= 0.866 Adjusted R2=0.837 F = 29.140 df1 = 4 df2 = 18

DurbinWatson

2.065

n = 23 Except the Foreign Direct Investment on agriculture, other variables conformed to the economic a priori expectation. A 100 percent point increase in bank loans, government capital expenditure and the agricultural credit 10

guarantee scheme lead to about 36%, 50% and 36% increase in agricultural output as influenced by each variable respectively. And a 100 percent point increase in foreign investment in agricultural leads to 35% fall in agricultural output in Nigeria. Comparing the calculated t-value of each variable and the theoretical tvalue of 1.730, other variables are significant at 90 percent level of significant except the foreign direct investment that is not significant at that level of significance. With the four variables employed, we can explain 87 percent of the systematic variation in agricultural output in Nigeria. This result is quite good. The remaining 13 percent may be explained by other variables that could influence agricultural output though, not specified in the model. Such variables could include fertilizer, pesticide, rainfall, soil fertility, availability of farmland and the demand for agricultural products. The F-value is highly significant at 95% level of significance. The result also suggests the absence of autocorrelation and multicollineariity in the model. However, this satisfies the desirable properties of unbiasedness, efficiency and consistency in the use of OLS. Conclusions and Recommendations Conclusion An increase in credit supply through the approval of commercial banks’ loans and advances, government capital expenditure on agriculture and agricultural credit guarantee scheme fund lead to increase in the output of agricultural commodities in Nigeria. Also, over the period, foreign direct investment on agriculture has not been significant in increasing the output of the sector. The study also concluded that the sector experienced a slight increasing return to scale over the years. Despite this, the rate of increase is not enough to meet the challenges facing the agricultural sector vis-à-vis , food insecurity over the years. 11

Recommendation The study confirmed the use of credit supplied to agricultural sector in Nigeria as a panacea for the growth in the sector. The government of Nigeria should see agriculture as the core of economic activities in terms of its employment and income generation; inter linkages with other sectors of the economy. Above all, is the supply of food to the teaming population. As factor identified as the determinants of agricultural output, government should make policies that will direct credit inflows through banks’ loans and advances to the sector, good percentage of government budget should be made available for agricultural activities. To encourage foreign investors to the sector, government should make policies that can strengthen PublicPrivate-Partnership in the sector and a conducive economic atmosphere for their existence. It is recommended that government should monitor credit meant for agriculture purpose to facilitate the efficient utilization of the credit. This study serves as baseline information to policy makers in the formulation of policy measures on credit administration, allocation and provision in the agricultural sector in Nigeria.

Reference 12

Aigokhan B.E. (2001), “Resuscitating Agricultural Production for Exports.” Cited In CBN Proceedings of the 10th Annual Conference of the Zonal Research Units. Akpan,H. Ekpo (1999), Public Expenditure and Economic Growth in a Petroleum Based Economy: Nigeria (1960-1992). International Journal of Social Sciences, Faculty of Social Sciences, University Uyo, Vol. 1, No. 1. Anyanwu J.C.,Oyefusi A., Oikhenan and Dimowo F.A. (1997), The Structure of Nigerian Economy (1960-1977), Joanee Educational Publishers Ltd. CBN (2003), Agricultural Development: Issues of Sustainability, Contemporary Economic Policy in Nigeria, Central Bank of Nigeria 2003, pp 185-213. CBN (2007), Annual Report and Statement of Accounts for the Year Ended 31st December, 2007. CBN (2008), Central Bank of Nigeria Statistical Bulletin, 50 years Anniversary Editions, December, CBN (2008), Economic Report for the First Half of 2008. Duong P.B. and Izumida Y. (2002), Rural Development Finance in Victsnsam, a Mciroeconometric Analysis of Household Surveys World Development, Vol. 30 (2). Ekechi A. O. (1996), “Interest Rate Policy on Bank Lending Behaviour”. CBN Economic And Financial Review Vol. 35, No. 2. Ekpedu I. D. (2006), Review of The Agricultural Sector in Nigeria (19601989). African Journal of Economy and Society. Vol. 7 No. 1. Isijola C.O. (2000), “Impact Of Financial Sector Reform On The Supply And Demand For Agricultural Credit In Nigeria”. First Bank Plc. Bi-Annual Review Vol. 8, No. 16. Iyoha, M. A. (2004), Applied Econometrics, Second Edition, Mindex Publisher Benin City. 13

Jhingan M.L. (2003), Macroeconomic Theory. 11th Revised Edition, Vrinda Publication (P) Ltd. Delhi. Nanna O.J. (2001), “Bank Lending Behaviour and Output Growth: An Empirical Analysis with implication on Monetary Policy in Nigeria”. CBN Economic and Financial Review, Vol. 40, No. 3. Obiechimina M. E. (2007), Improving the Agricultural Sector towards Economic Development and Poverty Reduction in Nigeria. CBN Bullion, Vol. 31, No. 4, pp 66-86. Oguamanam, H. M. (1996), “Commercial Bank Credit to Agricultural Sector in Nigeria”. Paper presented at The 17th Annual Conference of CBN Agricultural Credit Officers held at Hill Station Hotel, Jos. Ojameruaye E. O. and Oikhenan H. E. (2001), First Course in Econometrics. Published by H. Hennas Universal Service, Benin City. Otu and Balogun (1991), “Credit Policy and Agricultural Development in Nigeria” CBN Economic and Financial Review, Vol. 29, No. 2, June. pp 138155. Rahji M. A.Y and Fakayode S.B. (2009). “A Multinomial Logit Analysis of Agricultural Credit Rationing by Commercial Banks in Nigeria”. International Journal of Finance and Economics. Eurojournals Publishing, Inc. Rodolphe, Blavy (2005), Monitoring and Commitment in Bank Behaviour. IMF Working Paper, Western Hemisphere Department.

Lending

Shenggen Fan, Peter Hazell and Sukdw Throat (1998), Government Spending Growth and Poverty: An Analysis of Interlinkages in Rural India. Environmental and Production Technology Division. International Food Policy Research Institute, 2033 K Street N.W. Washington D.C. 20006 USA. Singh I; Squire L; and Strauss J. (1986), Agricultural House Hold Models, Extension Application and Policy , London. the Folm Hopkins University Press.

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Sloinnen, J. F. M And How , H. R. (1999), Agricultural Credit Problems and Policies During the Transition to a Market Economy in Central and Eastern Europe Food Policy, 24 (1999). Stiglits, J. (1993) “Incentive Organizational Structures and Contractual Choice in the Reform of Socialist Agricultural” in Beaverman, A; Brooks, K, Caki, C (Eds) The Agricultural Transition in Central and Eastern Europe and former USSR. World Bank, Washington D.C. Trzeciak-Duval A. (2003), Agricultural Finance and Credit InfrastructureConditions, Policies and Channels. Agricecon, - Czech, 2003(3): 106-112.

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APPENDIX I COMMERCIAL BANKS’ LOANS AND ADVANCES, GOVERNMENT CAPITAL EXPENDITURE, AGRICULTURAL CREDIT GUARANTEE SCHEME FUND, FOREIGN DIRECT INVESTMENT AND MAJOR OUTPUT OF AGRICULTURAL COMMODITIES IN NIGERIA Year Major Commercial Government Agricultural Foreign Output Of Banks’ Loans Capital Credit Direct Agricultural And Advances Expenditure Guarantee Investment Commodities Scheme Fund In Agricultural (‘000 tones) N’Mill N’Mill N’Mill N’Mill

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

9200.0 9164.0 9849.0 10754.0 11364.0 11892.0 12227.0 11456.0 11448.0 11270.0 12891.0 13042.0 14302.0 1476.0 15230.0 15367.0 15645.0 16735.7 20389.6 17752.8 18385.9 18505.9 18882.5

1830.3 2427.1 3066.7 3470.5 4221.4 5012.7 6978.9 10753.0 17888.8 25278.7 33264.1 27939.3 27180.7 118518.3 146504.5 200856.2 227617.6 242185.7 261558.6 262005.5 49393.4 82212.0 520311.0

892.5 365.1 595.7 981.5 1758.5 551.2 763.0 1820.0 2800.1 4691.7 3882.8 6247.4 8876.6 6912.6 6761.7 57879.0 32364.4 8610.9 48047.8 7939.4 15176.8 22618.7 29958.3

68417.4 102152.7 118611.0 129300.3 98493.4 82107.4 91953.0 80845.9 91821.1 163938.6 243608.0 244025.2 217699.0 246993.5 357832.0 810821.1 1062391.8 1894281.4 3308704.3 706969.0 4265066.3 4427868.9 6721074.6

128.2 117.3 128.9 134.8 334.7 382.8 386.4 1214.9 1208.5 1209.0 1209.0 1209.0 1209.0 1209.0 1209.0 1209.0 1209.0 1209.0 1209.0 1209.0 1209.0 1329.9 1397.2

Source: Central Bank of Nigeria Statistical Bulletin, 50years Special Anniversary Edition, December, 2008.

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APPENDIX II

COMMERCIAL BANKS’ LOANS AND ADVANCES, GOVERNMENT CAPITAL EXPENDITURE, AGRICULTURAL CREDIT GUARANTEE SCHEME FUND, FOREIGN DIRECT INVESTMENT AND MAJOR OUTPUT OF AGRICULTURAL COMMODITIES IN NIGERIA Year Major Output Commercial Government Agricultural Foreign Of Banks’ Loans Capital Credit Direct Agricultural And Expenditure Guarantee Investment Commoditie Advances Scheme Fund On s Agricultural N’Mill N’Mill N’Mill N’Mill (‘000 tones)

198 6 198 7 198 8 198 9 199 0 199 1 199 2 199 3 199 4 199 5 199 6 199 7 199 8 199

9.127 9.123 9.105 9.283 9.338 9.384 9.411 9.346 9.346 9.330 9.464 9.476 9.568 7.297 9.631 9.640 9.658 9.725 9.923 9.784 9.819 9.826 9.846

7.512 7.795 8.028 8.152 8.348 8.520 8.851 9.283 9.742 10.138 10.412 10.238 10.210 11.683 11.895 12.210 12.335 12.397 12.474 12.476 10.808 11.317 13.162

6.794 5.900 6.390 6.889 7.472 6.312 6.637 7.507 7.937 8.454 8.264 8.740 9.091 8.841 8.819 10.966 10.385 9.061 10.780 8.980 9.628 10.027 10.308

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11.133 11.534 11.684 11.770 11.498 11.316 11.429 11.300 11.428 12.007 12.403 12.405 12.291 12.417 12.788 13.606 13.876 14.454 15.012 13.469 15.266 15.303 14.334

4.854 4.765 4.859 4.904 5.813 5.948 5.957 7.102 7.097 7.098 7.098 7.098 7.098 7.098 7.098 7.098 7.098 7.098 7.098 7.098 7.098 7.193 7.242

9 200 0 200 1 200 2 200 3 200 4 200 5 200 6 200 7 200 8 Computed Logarithm* Source: Central Bank of Nigeria Statistical Bulletin, 50years Special Anniversary Edition, December, 2008.

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